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FPGA Implementation of Floating-Point Multiplier Employing Carry-Lookahead Adders

FPGA Implementation of Floating-Point Multiplier Employing C...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Abhay Sharma Md. Irfanul Hasan Devesh Tiwari Graphic Era (Deemed to be University) Dehradun India Graphic Era Hill University Dehradun India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

For hardware implementation of digital signal processing algorithms, the key components are multipliers. The design of multipliers plays an important role in overall system architecture. For algorithms requiring a dynamic range of data, floating-point representation is favoured over fixed-point. Nevertheless, floating-point multipliers provide difficulties for designers because of the large latency and space that they need. In this work, we suggest a more efficient method of floatingpoint multiplication with respect to both time and space. The bottleneck in the floating-point multiplier design is the Mantissa multiplication which is implemented through fast tree multipliers. Instead of using half-adders and full-adders as in conventional Wallace or Dadda tree multipliers, Carry-lookahead adders are used as compressors in the proposed structure, which resulted in a better partial product reduction in mantissa multiplication. The proposed design is validated through FPGA implementation using Verilog HDL description. It was observed that an improvement of 8.5% with respect to Wallace and an improvement of 9.9% with respect to Dadda is obtained in terms of latency.

关键词： Heuristic algorithms Signal processing algorithms Systems architecture Digital signal processing Dynamic range Hardware Compressors Hardware design languages field programmable gate arrays Adders

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Development and Analysis of Gate Underlap GaN Gate-All-Around FETs

Development and Analysis of Gate Underlap GaN Gate-All-Aroun...

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Smart Systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Sneha Singh Priya Devi Rudra Sankar Dhar Dept. of Electronics and Communication Engineering National Institute of Technology Mizoram Aizawl

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Gallium Nitride (GaN)-based Gate-All-Around field Effect Transistors (GAAFETs) with advanced Gate Stack (GS) engineering have emerged as leading candidates for nextgeneration energy-efficient electronics due to their superior material properties, including high electron mobility, wide bandgap, and excellent thermal stability. This study examines the DC performance of GaN-based GAAFETs, focusing on the integration of high-k dielectric spacers and gate underlap engineering to optimize key parameters such as On-Current, leakage current, switching speed, and subthreshold swing (SS). The incorporation of high-k spacers and gate underlap effectively suppresses short-channel effects (SCEs), improving scalability and energy efficiency. These advancements align with the IrdS 2025 2nm technology roadmap, ensuring enhanced electrostatic control for ultra-low-power, high-speed applications in IoT and 5G networks. The proposed device architecture exhibits substantial performance enhancements, making GaN-based GAAFETs highly suitable for digital and RF applications in sustainable electronics. Such innovations drive the advancement of semiconductor technology, catering to the increasing demand for energy-efficient, highperformance solutions in modern electronic systems.

关键词： Performance evaluation Simulation field effect transistors Switches Logic gates Energy efficiency Dielectrics High-k dielectric materials Gallium nitride Thermal stability

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Generative AI in Education: Developing Personalized Learning Experiences with Hyperspherical Variational Self-Attention Autoencoder 3

Generative AI in Education: Developing Personalized Learning...

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3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Han, Xue Li, Zhixiang Zhang, Wenchuan Fan, Wentao Guangdong Provincial Key Laboratory Irads Department of Computer Science Beijing Normal University-Hong Kong Baptist University United International College Guangdong Zhuhai China

ISBN: (纸本)9798400712692

With the advancement of artificial intelligence (AI) technology, content generation has sparked a transformative revolution in the field of education. In traditional education, teachers often spend a lot of time preparing lessons, because students have varying levels of proficiency, teachers need to consider whether the teaching content is suitable for everyone. Generative AI provides an effective solution to this problem by automatically generating personalized, high-quality educational content, which not only alleviates the burden on teachers but also enhances students' learning outcomes. This study focuses on a novel deep generative model - a framework based on Variational Autoencoders (VAE) and the self-attention mechanism (Transformer). We propose a model called the Hyperspherical Variational Self-Attention Autoencoder (HVSAE), which aims to generate personalized learning content based on students' learning situations, thereby reducing the burden on teachers and improving learning outcomes. The experimental results indicate that the model can generate high-quality educational resources, providing important support for achieving more personalized and efficient education. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Generative artificial intelligence HVSAE Personalized education Self-attention mechanism VAE

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Implementation of Column Line Code ECC for Image Error Correction

Implementation of Column Line Code ECC for Image Error Corre...

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Smart Systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Hemanth Prasad N Pooja Kenchetty Department of Electronics and Communication Engineering Amrita School of Engineering Bengaluru India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Nowadays, raw remote sensing images contain a significant amount of distortions; therefore, they cannot be used directly for further image analysis. Therefore, image error correction is a process of correcting the visibility of these images. In this project, the Column Line Code (CLC) Error Correction Code (ECC) is used for the process of the image error correction. The binarisation is automatically done using an online tool, based on which the inputs for the design are generated. The CLC is coded for a $2 \times 2$ matrix of data bits using Verilog HDL and verified exhaustively, for which the binarized data is given as the input for the reference, which is encoded and analyzed. Further, the error-incorporated inputs are encoded and compared in the CLC ECC decoding technique and corrected. The waveforms of these designs are verified using ModelSim, and the synthesis of the design is obtained using the Cadence Genus tool for a $4 \times 4$ and $2 \times 2$ standard CLC ECC. Similarly, the synthesis and implementation are performed using the Xilinx Vivado Tool for the Zynq ZC702 Evaluation board for a $4 \times 4$ standard CLC ECC and the $4 \times 4$ CLC ECC using a $2 \times 2$ standard CLC ECC, to analyze the design as per the hardware implementation using a FPGA. The $4 \times 4$ CLC ECC using $2 \times 2$ standard CLC ECC results in an enhanced design with a comparison of the $4 \times 4$ standard CLC ECC as per the reports analyzed from the synthesis.

关键词： Image analysis Smart systems Distortion Hardware Error correction codes Error correction Hardware design languages Standards Remote sensing field programmable gate arrays

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Federated Deep Learning for Cybersecurity and Intrusion Detection in Decentralized Networks

Federated Deep Learning for Cybersecurity and Intrusion Dete...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： Naeem Mia Jubair Ahmed Nabin Suzad Mohammad Mahedi Hasan Fahim Shakil Tamim Dipta Mohon Das Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The advancements in modern technologies demonstrate a growing trend in cyber attacks with complex patterns. These attacks can take various complex forms due to the diverse nature of data. To tackle this growing threat, ensuring data diversity is essential. However, contemporary works utilizing centralized model training face the challenge of maintaining data privacy. In this study, a federated deep learning based privacy-preserving architecture for Network Intrusion Detection is presented. This approach combines CNN and LSTM architecture within a Federated Learning (FL) framework. The framework was evaluated on a benchmark dataset CIC-IDS2017, encompassing seven classes - DoS, Portscan, Infiltration, Brute-force, Bot, Web Attack, and Benign. This framework demonstrates robustness in accurately detecting all the attack types with an accuracy of over 99.36%. This work will contribute to the cyber security field by addressing critical gaps in existing research and providing a robust solution for intrusion detection in a privacy-preserving manner.

关键词： Deep learning Data privacy Accuracy Federated learning Network intrusion detection Computer architecture Benchmark testing Chatbots Robustness Long short term memory

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Advanced Overhead field Passage Indicator for High-Current Power Lines

Advanced Overhead Field Passage Indicator for High-Current P...

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Smart Systems for applications in Electrical Sciences (ICSSES), international conference on

作者： Adarsh C Kiranmayi G R Chandrashekhara B G Venugopal A Dept. of EIE SIT Tumakuru SM Electronic Technologies Pvt Ltd

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

This work presents the development and implementation of an Advanced Overhead field Passage Indicator (FPI) designed for high-current power lines. Traditional FPIs face challenges such as limited accuracy, susceptibility to environmental factors, and difficulty in distinguishing normal variations from actual faults, leading to inefficiencies and safety risks in power management. The proposed system overcomes these limitations by integrating state-of-the-art sensing technology and robust algorithms for precise, real-time power line monitoring. It features an RL78/I1C microcontroller with a 24-bit delta-sigma ADC and RAA788000 Rogowski coil-based current sensors in a differential-stacked configuration, minimizing noise interference and improving measurement accuracy. The sensors are divided into two measurement ranges (0A-500A and 500A-1000A) to enhance sensitivity across varying current loads. Although designed to measure up to 5000A, testing is conducted up to 1000A due to developer-imposed constraints, and validation using a portable 3-phase energy meter test bench confirmed an average accuracy of 99.9%. This innovative approach enhances operational efficiency by enabling automated fault detection, real-time data acquisition, and analysis, making it a valuable tool for improving power transmission reliability.

关键词： Accuracy Microcontrollers Current measurement Fault detection Power system management Electrical fault detection Real-time systems Reliability Monitoring Testing

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FRMT: RMT with Frequency Feature learning Framework for land cover classification 3

FRMT: RMT with Frequency Feature learning Framework for land...

引用

3rd international conference on Environmental Remote Sensing and Geographic Information technology, ERSGIT 2024

作者： Ou, Zhihui Chen, Fang Wang, Lei Yu, Bo Wang, Ning School of Computer Science and Information Security Guilin University of Electronic Technology Guilin 541004 China International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China

ISBN: (纸本)9781510690219

The classification of land cover is of great significance in the management and sustainable development of land resources. In recent years, deep learning methods have made remarkable progress in the field of remote sensing image processing. However, due to the complex difference in structure and scale of surface objects in remote sensing images, most of the current methods are prone to the problem of unclear boundary extraction, and the classification results of different scale surface objects are poor. In this letter, we proposed FRMT by introducing frequency domain information into RMT framework. A frequency feature learning module (FFLM) is proposed. The frequency domain features are decomposed into high frequency and low frequency features, and the frequency features are learned by spiking convolution to strengthen the model's learning of boundary and different scale features. We performed experimental validation on the very challenging multi-class remote sensing dataset GID-15 and Huawei Ascend Cup competition dataset, and the experimental results show that our model achieves the best results compared to five other recently proposed models, which mIoU achieves 77.91% and 71.85%, respectively. © 2025 SPIE.

关键词： deep learning high resolution images land cover classification remote sensing

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Design Innovations in TFETS: Enhancing Performance and Sensitivity Through Charged Plasma, Dual-Gate, and Work Function Techniques

Design Innovations in TFETS: Enhancing Performance and Sensi...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Kaushal Kumar Challa Harshavardhan Reddy Dasari Ramesh Babu Vinay Kumar Ajay Kumar Aditya Jain ECE Department Graphic Era Deemed to be University Dehradun India ECE Department Jaypee Institute of Information Technology Noida India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

In recent years, Tunnel field-Effect Transistors (TFETs) have garnered considerable attention as promising candidates to achieve the new generation of electronics that require high performance, low power, and scalability as they can provide increased energy efficiency due to the band-to-band tunneling (BTBT) mechanism and have a steep subthreshold slope. Nonetheless, the standard TFETs suffer from low on-state current along with short-channel effects and processing difficulties. However, to exploit these emerging potentials and improve TFET performance and capabilities junction less TFET, dual material gate, heterojunction architectures and driven work-function engineering innovations have arisen creating room for the use of TFETs in biosensing applications as well as high-frequency circuit designs. This review highlights four of the most significant advancements that comprise: Junction less and Core-Shell Nanotube TFETs for high-sensitivity detection of label-free biomolecules, Dual- Material Gate Heterojunction TFETs for enhanced on current and switching speeds, Symmetric Double-Gate Charge Plasma TFETs for simplified doping-less fabrication, and Drain Work Function Engineered TFETs to enable ambipolar suppression and improve RF performance. We then analyze the structure, functions, and applications as well the tradeoffs in terms of scalability, stability and reliability for each design.

关键词： Radio frequency Performance evaluation Technological innovation TFETs Logic gates Tunneling Heterojunctions Plasmas Leakage currents Transistors

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Remote Sensing-Based Agricultural Damage Assessment: A Semantic Segmentation Method for Analyzing Corn field Changes 25

Remote Sensing-Based Agricultural Damage Assessment: A Seman...

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Proceedings of the 2025 3rd international conference on Communication Networks and Machine Learning

作者： Meng Wu Zeyu Li Yu Wang Yi Wei Ying Chen Xiaoyue Tu School of Information Science and Technology Sanda University Shanghai China Computer Science and Artificial Intelligence Department Leiden University Leiden Holland Teaching Department Kede Future (Shanghai) Education Technology Co. Ltd. Shanghai China

ISBN: (纸本)9798400713231

The increasing impact of natural disasters on agricultural production highlights the vital need for accurate estimation of changes in crop cultivation areas as pertains to agricultural recovery and formulation of food security strategies. In this study, the authors apply an integrated approach of remote sensing data and semantic segmentation technology by taking the June 2023 event in Illinois, termed the derecho event, as a case study. DeepCBAMNet is applied to analyze the pre-and post-disaster remote sensing images, yielding an overall model accuracy of 93.39% and a mean Intersection over Union (mIoU) of 81.88% on the training dataset. The study reports an area affected by the disaster of 69,352.01 acres with a 91.98% accuracy rate when compared with that reported by the United States Department of Agriculture (75,401.60 acres). The methodology is a key advancement in the assessment of agricultural disasters, as it gives a new technical approach to the rapid and accurate assessment of disaster impact, thus allowing government authorities to put in place more productive post-disaster recovery strategies and agricultural policies.

关键词： Agricultural Disaster Monitoring

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Advanced Breast Cancer Classification Using a Multi-Branch Spectral Channel Attention Network in Histopathology Imaging

Advanced Breast Cancer Classification Using a Multi-Branch S...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Shunmuga Sundari. M Mitul Patel Adusupalle Muni Raju Shailesh Rastogi Department of Artificial Intelligence and Data Science Chennai Institute of Technology Kundrathur Chennai India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Parul University Vadodara India Balaji Institute of International BIIB) Pune India Department of Finance Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In the field of medical image analysis, deep learning has revolutionized the diagnosis and classification of diseases, notably breast cancer. Current methods predominantly utilize convolutional neural networks (CNNs) to analyze spatial features within histopathology images. Despite their efficacy, these methods often overlook the rich frequency domain information that texture-based images inherently possess. Recognizing this gap, our research introduces the Multi-Branch Spectral Channel Attention Network (MbsCANet), a novel architecture designed to enhance breast cancer classification by integrating frequency domain analysis with spatial domain insights. This approach leverages a dual-channel mechanism that combines low and high-frequency features extracted through the two-dimensional discrete cosine transform (DCT). By doing so, MbsCANet not only preserves phase information but also enriches the contextual data crucial for accurate classification. We rigorously tested our model using the BreakHis dataset, achieving unprecedented accuracy levels of 99.01% at image-level and 98.87% at patient-level classification. These results significantly outperform traditional spatial-domain-focused models. The visualization of the model's processing layers further validates its effectiveness, presenting a compelling case for the integration of spectral analysis in medical imaging diagnostics.

关键词： Accuracy Image analysis Histopathology Frequency-domain analysis Computer architecture Feature extraction Breast cancer Convolutional neural networks Discrete cosine transforms Medical diagnostic imaging

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